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Improving Attention and Managing Attentional Problems

2001· article· en· W1734836448 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2001
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychologyPsychosocialVariety (cybernetics)RehabilitationProcess (computing)Cognitive psychologyPsychotherapistNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Research and clinical experience in the field of brain injury rehabilitation have focused quite extensively on the need and potential to retrain attentional skills that are commonly affected by acquired brain injury. Four approaches to managing attention impairments that have emerged from this literature include attention process training, training use of strategies and environmental support, training use of external aids, and the provision of psychosocial support. Most often, several of these will be used in combination. For example, a therapy regimen might include attention process training emphasizing specific components of attention (e.g., sustained attention), in conjunction with training in pacing techniques, and psychosocial support, where the client keeps behavioral logs and discusses insights gained from tracking attention successes and attention lapses. Although there are as yet little data with regard to the effectiveness of these approaches in adults with developmental disorders of attention, there is a growing literature suggesting they may be effective in children and adolescents with ADHD. Further investigation of the application of such techniques in adults with a wide variety of attention disorders, including developmental disorders, would be valuable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.363
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it